Property-space similarity modeling   

Abstract: The present invention relates to modeling systems for designing consumer products and selected components for use in consumer products, consumer products and components selected by such models and the use of same. In addition, a system that minimizes the risks associated with a collaboration yet promotes the rapid advance of the subject/goal of the collaboration is disclosed.

This application is a continuation of prior co-pending International Application No. PCT/IB2009/052956 filed Jul. 7, 2009, designating the United States.

FIELD OF THE INVENTION

The present invention relates to modeling systems for designing consumer products and selected components for use in consumer products and components selected by such models and the use of same.

BACKGROUND OF THE INVENTION

Consumer goods are typically designed and/or formulated using empirical methods or basic modeling methodologies. Such efforts are time consuming, expensive and, in the case of empirical methodologies, generally do not result in optimum designs/formulations as not all components and parameters can be considered. Furthermore, aspects of such methods may be limited to existing components. Thus, there is a need for an effective and efficient methodology that obviates the short comings of such methods. New modeling processes have been disclosed, (See for example USPA 2008/0040082 A1). Such processes are an improvement, yet further improvements are desired as the performance of many consumer products and the components thereof is the function of multiple simultaneous properties. Thus there is a need for improvements that allow for efficient multidimensional modeling systems. The modeling systems of the present invention meet the aforementioned need and, in addition, can be used to select or design new and superior formulation component that can be used to produce new and superior formulations.

In addition, many modeling efforts require collaboration, for example, the collaboration of a raw material supplier and a formulator. In many cases, such collaboration requires the exchange of confidential information. As receiving and supplying confidential entails risk for both the receiving party and the supplying party—particularly when one or more of the parties has multiple collaborations going simultaneously—the parties typically desire to minimize the confidential information that is exchanged. This desire typically conflicts with the parties need to rapidly advance the subject/goal of the collaboration. Thus, what is needed is a system that minimizes the risks associated with a collaboration yet promotes the rapid advance of the subject/goal of the collaboration. Such a system is disclosed herein.

SUMMARY

The present invention relates to modeling systems for designing consumer products and selected components for use in consumer products, consumer products and components selected by such models and the use of same. In addition, a system that minimizes the risks associated with a collaboration yet promotes the rapid advance of the subject/goal of the collaboration is disclosed.

DETAILED DESCRIPTION

As used herein “consumer products” includes, unless otherwise indicated, articles, baby care, beauty care, fabric & home care, family care, feminine care, health care, snack and/or beverage products or devices intended to be used or consumed in the form in which it is sold, and is not intended for subsequent commercial manufacture or modification. Such products include but are not limited to home decor, batteries, diapers, bibs, wipes; products for and/or methods relating to treating hair (human, dog, and/or cat), including bleaching, coloring, dyeing, conditioning, shampooing, styling; deodorants and antiperspirants; personal cleansing; cosmetics; skin care including application of creams, lotions, and other topically applied products for consumer use; and shaving products, products for and/or methods relating to treating fabrics, hard surfaces and any other surfaces in the area of fabric and home care, including: air care, car care, dishwashing, fabric conditioning (including softening), laundry detergency, laundry and rinse additive and/or care, hard surface cleaning and/or treatment, and other cleaning for consumer or institutional use; products and/or methods relating to bath tissue, facial tissue, paper handkerchiefs, and/or paper towels; tampons, feminine napkins; products and/or methods relating to oral care including toothpastes, tooth gels, tooth rinses, denture adhesives, tooth whitening; over-the-counter health care including cough and cold remedies, pain relievers, pet health and nutrition, and water purification; processed food products intended primarily for consumption between customary meals or as a meal accompaniment (non-limiting examples include potato chips, tortilla chips, popcorn, pretzels, corn chips, cereal bars, vegetable chips or crisps, snack mixes, party mixes, multigrain chips, snack crackers, cheese snacks, pork rinds, corn snacks, pellet snacks, extruded snacks and bagel chips); and coffee and cleaning and/or treatment compositions

As used herein, the term “cleaning and/or treatment composition” includes, unless otherwise indicated, tablet, granular or powder-form all-purpose or “heavy-duty” washing agents, especially cleaning detergents; liquid, gel or paste-form all-purpose washing agents, especially the so-called heavy-duty liquid types; liquid fine-fabric detergents; hand dishwashing agents or light duty dishwashing agents, especially those of the high-foaming type; machine dishwashing agents, including the various tablet, granular, liquid and rinse-aid types for household and institutional use; liquid cleaning and disinfecting agents, including antibacterial hand-wash types, cleaning bars, mouthwashes, denture cleaners, car or carpet shampoos, bathroom cleaners; hair shampoos and hair-rinses; shower gels and foam baths and metal cleaners; as well as cleaning auxiliaries such as bleach additives and “stain-stick” or pre-treat types.

As used herein the term “non-polymer consumer product component” does not include polymers.

As used herein, the term “situs” includes paper products, fabrics, garments and hard surfaces.

As used herein, the articles “a”, “an”, and “the” when used in a claim, are understood to mean one or more of what is claimed or described.

Unless otherwise noted, all component or composition levels are in reference to the active level of that component or composition, and are exclusive of impurities, for example, residual solvents or by-products, which may be present in commercially available sources.

All percentages and ratios are calculated by weight unless otherwise indicated. All percentages and ratios are calculated based on the total composition unless otherwise indicated.

It should be understood that every maximum numerical limitation given throughout this specification includes every lower numerical limitation, as if such lower numerical limitations were expressly written herein. Every minimum numerical limitation given throughout this specification will include every higher numerical limitation, as if such higher numerical limitations were expressly written herein. Every numerical range given throughout this specification will include every narrower numerical range that falls within such broader numerical range, as if such narrower numerical ranges were all expressly written herein.

A process, of selecting a consumer product component for use in a consumer product, that may comprise: a) comparing two or more independent properties of an actual or hypothetical initial consumer product component with the same independent properties of one or more actual or hypothetical additional consumer product components; b) selecting those one or more actual or hypothetical additional consumer product components in the proximity of said suitable actual or hypothetical initial consumer product component when said two or more independent properties of said actual or hypothetical initial consumer product component and said actual or hypothetical additional consumer product components are mapped via calculation or graphically in a multi-dimensional space having the same dimensions as the number of said independent properties; c) sorting the list of actual or hypothetical additional consumer product components in order of increasing distance and selecting for consideration those materials with shortest distance to the initial actual or hypothetical initial consumer product component; d) optionally, using the output of Step b.) to refine the selection of a new actual or hypothetical initial consumer product component by repeating Steps a) through b) e) optionally repeating Steps a) through c) is disclosed.

In one aspect of the aforementioned process, said two or more independent properties may be selected from the group consisting of Amine-assisted perfume delivery, Western-European washing conditions, 5-weeks post-dry storage model (WE-5); Amine-assisted perfume delivery, North-American washing conditions, 1-week post-dry storage model (NA-1) model; Polymer amine-assisted perfume delivery, Western-European washing conditions, 1-day post-dry storage model (WE-1) model; vapor pressure; boiling point; betaCyclodextrins complex stability constant; malodor reduction value; SDS micelle-water partition coefficient; Henrys Law (air-water partition) coefficient; odor character; critical micelle concentration; dynamic surface tension; grease/oil stain removal; grass stain removal; clay/soil stain removal; biodegradability; chemical reactivity; odor masking; Kovats index; packaging compatibility; LogP; ammonia odor reduction; flash point; aqueous solubility; perfume ingredient color/odor stability decision model; liquid dish product-air perfume raw material partition coefficient; shampoo product-air perfume raw material partition coefficient; hair conditioner product-air perfume raw material partition coefficient; and intrinsic aqueous solubility.

In one aspect of the aforementioned process, said proximity, d(x,y), may be determined by a method selected from computing a distance or dissimilarity coefficient using the following equation:

d  ( x , y ) = [ ∑ i = 1 m   x i - y i  r ] 1 / r where x=(x1, x2, . . . , xm) and y=(y1, y2, . . . , ym) represent two points in the m-dimensional space and wherein in the case of the distance measure for the city-block metric r=1, and wherein in the case of the distance measure for the Euclidean distance metric r=2.

In one aspect of the aforementioned process, said proximity may be determined by computing the Euclidean distance metric.

In one aspect of the aforementioned process, the consumer product component that is selected may be selected from the group consisting of a perfume, a surfactant, or a solvent.

In one aspect of the aforementioned process, a) said perfume may be selected for use in an Amine-Assisted Perfume Delivery

System, Polymer amine-assisted perfume delivery, betaCyclodextrins delivery system, a shampoo, an aircare product, a hair dye, a color and odor stable deodorant product, a liquid dish product, a candle or a microcapsule; b) said surfactant may be selected for use in a laundry cleaning product; and c) said solvent may be selected for use in a heavy duty liquid laundry detergent.

In one aspect of the aforementioned process, the values for said independent properties may be calculated, measured or obtained from a reference source.

In one aspect of the aforementioned process, said perfume may be selected for use in an Amine-Assisted Perfume Delivery System and said one or more independent properties may comprise; a.) NA-1 model; vapor pressure and octanol-water partition coefficient; and, optionally, boiling point; or b.) WE-5 model; vapor pressure and octanol-water partition coefficient; and, optionally, boiling point.

In one aspect of the aforementioned process, said perfume may be selected for use in a Polymer amine-assisted Perfume Delivery System and said one or more independent properties may comprise; WE-1 model; vapor pressure, and octanol-water partition coefficient; and, optionally, boiling point. In one aspect of the aforementioned process, said perfume may be selected for use in a betaCyclodextrins delivery system and said one or more independent properties may comprise betaCyclodextrin complex stability constants; and vapor pressure; and, optionally, malodor reduction value.

In one aspect of the aforementioned process, said perfume may be selected for use in a shampoo and said one or more independent properties may comprise SDS micelle-water partition coefficient; Henrys Law (air-water partition) coefficient; and vapor pressure; and, optionally, odor character.

In one aspect of the aforementioned process, said perfume may be selected for use in a hair dye and said one or more independent properties may comprise the octanol-water partition coefficient; chemical reactivity; vapor pressure; and ammonia odor reduction.

In one aspect of the aforementioned process, said surfactant may be selected for use in a laundry cleaning product and said one or more independent properties may comprise critical micelle concentration; dynamic surface tension; grease/oil stain removal; grass stain removal; clay/soil stain removal; and biodegradability.

In one aspect of the aforementioned process, said perfume may be selected for use in a color and odor stable deodorant product and said one or more independent properties may comprise the perfume ingredient color/odor stability decision model, LogP, vapor pressure and odor masking.

In one aspect of the aforementioned process, said perfume may be selected for use in a liquid dish product and said one or more independent properties may comprise the liquid dish product-air perfume raw material partition coefficient, Henrys Law (air-water partition) coefficient, LogP and vapor pressure.

In one aspect of the aforementioned process, said perfume may be selected for use in a candle and said one or more independent properties may comprise Kovats index, LogP, and, optionally, odor masking.

In one aspect of the aforementioned process, at least one independent property may be determined by employing a technique selected from the group consisting of multiple linear regression, genetic function method, generalized simulated annealing, principal components regression, non-linear regression, projection to latent structures regression, neural networks, support vector machines, logistic regression, ridge regression, cluster analysis, discriminant analysis, decision trees, nearest-neighbor classifier, molecular similarity analysis, molecular diversity analysis, comparative molecular field analysis, Free and Wilson analysis, group contribution methods and combinations thereof.

In one aspect of the aforementioned process, said technique may be selected from the group consisting of multiple linear regression, genetic function method, generalized simulated annealing, principal components regression, non-linear regression, projection to latent structures regression, neural networks, support vector machines, logistic regression, ridge regression, cluster analysis, discriminant analysis, molecular similarity analysis, molecular diversity analysis, group contribution methods and combinations thereof.

In one aspect of the aforementioned process, said technique may be selected from the group consisting of multiple linear regression, genetic function method, generalized simulated annealing, projection to latent structures regression, neural networks, cluster analysis, discriminant analysis, molecular similarity analysis, molecular diversity analysis, group contribution methods and combinations thereof.

In one aspect of the aforementioned process, said consumer product component may be selected from the group consisting of surfactants, chelating agents, dye transfer inhibiting agents, dispersants, and enzyme stabilizers, catalysts, bleach activators, sources of hydrogen peroxide, preformed peracids, brighteners, dyes, perfumes, carriers, hydrotropes, solvents and combinations thereof.

In one aspect of the aforementioned process, Steps a.) through c.) are repeated at least once.

In one aspect, any or all of the computations of the processes disclosed herein may be preformed by a computing device. Such computing device may be a portable device, for example, a laptop computer.

In one aspect, computing the distance in the multi-dimensional property space may be performed by entering the distance equation, for example, the Euclidean distance equation, into a spreadsheet program, for example, Excel® 2007 (MicroSoft, Redmond, WA 98052-7329) that is run on a computer.

The independent properties used in the present modelling system may be obtained by any of the means, including combinations there of, described below

In one aspect, the independent properties used in the present modelling system may be obtained from a reference including but not limited to a written and/or electronic document.

In one aspect, the independent properties used in the present modelling system may be obtained by measuring said independent properties.

In one aspect, the independent properties used in the present modelling system may be obtained by the use of a commercial or otherwise existing model comprising the steps of: a.) structure entry into a computer, said structure entry can be achieved via sketching using, for example, the following software such as: Sybyl® (Ver. 6.9, Tripos, Inc, St. Louis, Mo.); Cerius2® (Ver. 4.9, Accelrys, Inc., San Diego, Calif.); ChemFinder™ (Ver. 7.0, CambridgeSoft, Cambridge, Mass.); Spartan \'02 (Build 119, Wavefunction, Inc., Irvine, Calif.); CAChe™ (Ver. 5.0, Fujitsu America, Sunnyvale, Calif.); JME Molecular Editor©, or reading pre-stored structures, suitable non-limiting storage formats include SMILES strings; MDL® CTfile or SDF file, Tripos MOL and MOL2 file, PDB file, HyperChem® HIN file, CAChe™ CSF file, ; b.) generating 3D atomic coordinates as needed, said generation optionally employing a technique selected from the group consisting of 2D-3D converters, conformational analysis, conformational optimization or combination thereof, and can be achieved using, for example Concord® (Tripos, Inc, St. Louis, Mo.); Corina (Molecular Networks GmbH, Erlangen, Germany); Omega (OpenEye Scientific Software, Santa Fe, N.Mex.); Cerius2® (Ver. 4.9, Accelrys, Inc., San Diego, Calif.); Chem3D™ (Ver. 7.0, CambridgeSoft, Cambridge, Mass.); Spartan \'02 (Build 119, Wavefunction, Inc., Irvine, Calif.); CAChe™ (Ver. 5.0, Fujitsu America, Sunnyvale, Calif.), AMPAC™ (Ver. 7.0, Semichem, Shawnee Mission, Kans.), Hyperchem® (Ver. 7.5, Hypercube, Inc., Gainsville, Fla.); c.) calculating, one or independent properties using said commercial or otherwise existing model.

Suitable commercial models include, but are not limited to: CSLogWS™ (Version 3.0), CSLogD™ (Version 3.0), CSLogWSO™ (Version 3.0) and CSpKa™ (Version 3.0) supplied by ChemSilico™ (ChemSilico LLC, Tewksbury, Mass. 01876); logD (Version 12.0), logP (Version 12.0), pKa (Version 12.0), Aqueous Solubility (Version 12.0) and Boiling Point (Version 12.0) supplied by ACD/Labs (Advanced Chemistry Development, Inc, Toronto, Ontario, Canada M5C 1T4); and ClogP/CMR™ (version 5.0) supplied by BioByte Corp. (Claremont, Calif. 91711-4707).

Suitable existing models include, but are not limited to, Amine-assisted perfume delivery, Western-European washing conditions, 5-weeks post-dry storage model (WE-5); Amine-assisted perfume delivery, North-American washing conditions, 1-week post-dry storage model (NA-1) model; Polymer amine-assisted perfume delivery, Western-European washing conditions, 1-day post-dry storage model (WE-1) model; vapor pressure; boiling point; betaCyclodextrins complex stability constant; malodor reduction value; SDS micelle-water partition coefficient; Henrys Law (air-water partition) coefficient; critical micelle concentration; odor masking; Kovats index; perfume ingredient color/odor stability decision model; shampoo product-air perfume raw material partition coefficient; hair conditioner product-air perfume raw material partition coefficient. Such models are given below:

The following linear regression models are implemented using the general formula:

y = b 0 + ∑ i = 1 n  b i  m i

. . . where y is the property being computed, b0 is the y-intercept, n is the number of descriptors in the model, mi is the ith descriptor in the model, and bi is the coefficient for the ith descriptor. 1) Amine-assisted perfume delivery, Western-European washing conditions, 5-weeks post dry (WE-5). Output: log(Headspace response ratio). Descriptor source: winMolconn (Hall Associates Consulting, Ver. 1.0.1.3). Structure Preparation: 2D connection table (SDF format or SMILES).

Descriptor Coefficient

Download full PDF for full patent description/claims.




You can also Monitor Keywords and Search for tracking patents relating to this Property-space similarity modeling patent application.
###


Other recent patent applications listed under the agent :